Braids of Partitions for the Hierarchical Representation and Segmentation of Multimodal Images

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Abstract

Hierarchical data representations are powerful tools to analyze images and have found numerous applications in image processing. When it comes to multimodal images however, the fusion of multiple hierarchies remains an open question. Recently, the concept of braids of partitions has been proposed as a theoretical tool and possible solution to this issue. In this paper, we demonstrate the relevance of the braid structure for the hierarchical representation of multimodal images. We first propose a fully operable procedure to build a braid of partitions from two hierarchical representations. We then derive a framework for multimodal image segmentationrelying on an energetic minimization scheme conducted on the braid structure. The proposed approach is investigated on different multimodal images scenarios, and the obtained results confirm its ability to efficiently handle the multimodal information to produce more accurate segmentation outputs.

Documents

Bibtex (lrde.bib)

@Article{	  tochon.19.pr,
  author	= {Guillaume Tochon and Mauro {Dalla Mura} and Miguel Angel
		  Veganzones and Thierry G\'eraud and Jocelyn Chanussot},
  title		= {Braids of Partitions for the Hierarchical Representation
		  and Segmentation of Multimodal Images},
  journal	= {Pattern Recognition},
  volume	= 95,
  pages		= {162--172},
  year		= 2019,
  abstract	= {Hierarchical data representations are powerful tools to
		  analyze images and have found numerous applications in
		  image processing. When it comes to multimodal images
		  however, the fusion of multiple hierarchies remains an open
		  question. Recently, the concept of braids of partitions has
		  been proposed as a theoretical tool and possible solution
		  to this issue. In this paper, we demonstrate the relevance
		  of the braid structure for the hierarchical representation
		  of multimodal images. We first propose a fully operable
		  procedure to build a braid of partitions from two
		  hierarchical representations. We then derive a framework
		  for multimodal image segmentation, relying on an energetic
		  minimization scheme conducted on the braid structure. The
		  proposed approach is investigated on different multimodal
		  images scenarios, and the obtained results confirm its
		  ability to efficiently handle the multimodal information to
		  produce more accurate segmentation outputs.}
}